{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from joblib import load\n",
    "import numpy as np\n",
    "\n",
    "model = load(\"HousingPricePredicter.joblib\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "features = np.array(\n",
    "    [\n",
    "        [\n",
    "            -0.43942006,\n",
    "            3.12628155,\n",
    "            -1.12165014,\n",
    "            -0.27288841,\n",
    "            -1.42262747,\n",
    "            -0.24141041,\n",
    "            -1.31238772,\n",
    "            2.61111401,\n",
    "            -1.0016859,\n",
    "            -0.5778192,\n",
    "            -0.97491834,\n",
    "            0.41164221,\n",
    "            -0.86091034,\n",
    "        ]\n",
    "    ]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([22.508])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(features)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
